Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning.
gait biomechanics
knee joint moments
machine learning
neural network
time-series
Journal
Frontiers in bioengineering and biotechnology
ISSN: 2296-4185
Titre abrégé: Front Bioeng Biotechnol
Pays: Switzerland
ID NLM: 101632513
Informations de publication
Date de publication:
2022
2022
Historique:
received:
16
02
2022
accepted:
19
04
2022
entrez:
1
6
2022
pubmed:
2
6
2022
medline:
2
6
2022
Statut:
epublish
Résumé
Knee joint moments are commonly calculated to provide an indirect measure of knee joint loads. A shortcoming of inverse dynamics approaches is that the process of collecting and processing human motion data can be time-consuming. This study aimed to benchmark five different deep learning methods in using walking segment kinematics for predicting internal knee abduction impulse during walking. Three-dimensional kinematic and kinetic data used for the present analyses came from a publicly available dataset on walking (participants
Identifiants
pubmed: 35646876
doi: 10.3389/fbioe.2022.877347
pii: 877347
pmc: PMC9133596
doi:
Types de publication
Journal Article
Langues
eng
Pagination
877347Informations de copyright
Copyright © 2022 Boukhennoufa, Altai, Zhai, Utti, McDonald-Maier and Liew.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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